An encrypted payment message is received from a buyer relating to a transaction with a merchant. The payment message is parsed using a first parsing algorithm to obtain merchant identifying information. The merchant identifying information is associated with at least a second parsing algorithm or at least one settlement algorithm. The payment message is parsed using the second parsing algorithm to obtain payment information for the transaction. A first settlement algorithm is applied to the payment information to determine an adjusted interchange rate. The payment information including the adjusted interchange rate is submitted to a third party settlement processor on behalf of the merchant, using a payment algorithm. A second settlement algorithm applied to the payment information determines a buyer rebate which can be submitted to the settlement processor. A transaction result is reported to at least one of the merchant or the buyer.

A cross border payment (CBP) computing device for transmitting cross border data rich payments in real-time is provided. The CBP computing device receives, from an originating bank associated with a payor, a payment request message to transfer funds in real-time. The payment request message includes payment instructions and transaction information associated with a transfer amount. The CBP computing device determines that the payment request message is a cross border payment transfer from a first payment network for processing real-time payments within a first country to a second payment network for processing real-time payments within a second country, and retrieves, from a database, a set of rules based on a payment messaging standard and protocol associated with the second payment network. The CBP computing device reformats the payment request messaged based on the retrieved set of rules, and transmits the reformatted payment message to a receiving bank associated with a payee.

An apparatus for contract transactions, comprising: a contract object manager, managing a contract exchange rate Ph of a first asset A to a second asset B; a margin determiner, configured to allow a user to select one of assets thereof as a transaction margin; a transaction engine, configured to execute a transaction after receiving a transaction order from the user to the Ph; a position holding adjustment processor, configured to enable the amount of A, converted into by contract position holdings of the user, to be negatively correlated with the Ph, and the amount of B, converted into by the contract position holdings of the user, to be positively correlated with the Ph according to changes of the Ph; a transaction data memory, configured to store basic information of the user;and a liquidation processor, configured to initiate a liquidation to the user at a preset price.

A method for conversion of digital assets to fiat currency using a secondary blockchain and mirrored accounting includes: receiving transaction data corresponding to a first blockchain transaction processed using a first blockchain, the transaction data including a payer identifier, recipient identifier, and blockchain currency amount; validating an entity as an authorized entity associated with the payer identifier; determining a fiat currency amount based on the blockchain currency amount; generating a second blockchain transaction, the second blockchain transaction including an unspent transaction output, destination address associated with the authorized entity, and the blockchain currency amount and/or the fiat currency amount; confirming addition of the second blockchain transaction to a second blockchain; and initiating a transfer for the fiat currency amount from a first transaction account to a second transaction account, the second transaction account being associated with the authorized entity.

A method for processing a medium includes: obtaining check information from an image of a check as the medium; and performing combined processing of deposit processing and exchange processing based on the check information, the combined processing including at least one of check deposit processing and check exchange processing.

A transaction terminal provides processing for payment using a cryptocurrency during a transaction being conducted at the transaction terminal. The transaction terminal further provides processing for a refund transaction utilizing cryptocurrency.

Systems and methods for communication of electronic data in which one or more memory-coupled entity processors programmed to execute a stateless application that persists no data except configuration data in the stateless application and causes the at least one entity processor to receive data in a first format from a data source to which the stateless application executing on the at least one entity processor is loosely coupled; translate the received data to a second format for a data destination; and submit the data in the second format to the data destination to which the stateless application executing on the at least one entity processor is also loosely coupled.

The present invention relates to a driving method of a block chain-based health data management system. When personal health data such as health examination or prescription data and the like is uploaded, a genome portal: stores the personal health data as metadata together with genetic information; provides the personal health data so that a demand agency requiring the personal health data can use Zerocoin to purchase or read the personal health data through the genome portal; provides rewards in the form of mileage to individuals who share the data in the genome portal; ranks the individuals by the number of times the individuals have shared the data; automatically presents various personalized health solutions in the portal through AI according to the shared data; provides a rapidly growing personal genome prediction/diagnosis service through a portal system by using a blockchain-based MS decentralized identity (DID) in order to enhance the security of the email addresses and IDs of users; and ascribes value to personal health big data, such as genome data, medical diagnosis information, or social data, and so as to reduce the costs of personal genetic testing and diagnosis.

Provided is a blockchain-based crowdfunding device and a crowdfunding service method using the platform, the blockchain-based crowdfunding device, comprising: a blockchain unit comprising: a membership certification part configured to treat admission and certification of the patentee and the investor as members, and function to form an electronic wallet; an electronic coin issuance part configured to issue an electronic coin which can be traded in an electronic money exchange bureau and can be used in a scope of all the business carried out in the patent value coin platform; and a patent value coin issuance part configured to issue a value coin that is intended for securing a right and value owned by the patentee and investor and can be used for the right and value only when crowdfunding is performed in the crowdfunding device; and a funding service unit comprising: a patent art provision part configured to quantify value of a patent art, thereby calculating an amount of money corresponding to the quantifying value; a funding channel management part configured to connect the investor and the patentee with each other, cause them to hold mutual information in common, and provide a code corresponding to the patent art; and a service promotion management part configured to provide the patentee and the investor of a funding channel with various service promotions.

An exemplary healthcare services recommender implementation may be configured to recommend selectively redeemable bundled services comprising care for conditions predicted as likely by a machine learning model based on patient data input. The likely conditions may be predicted for a patient or a patient population comprising a plurality of patients. The machine learning model may comprise a neural network trained to determine a probability distribution of patient condition trends for a future time period based on patient data input. The patient data input may comprise data from an Electronic Medical Record (EMR), diagnosis, lab test, patient-provided information such as survey/symptom data, and/or wearable device data. The machine learning model may be trained using a training data set including at least healthcare outcomes sampled during a past time period from a plurality of patient healthcare episodes each comprising a plurality of procedures performed for the patient in at least one encounter.